Word Embedding, LookupTable, Word Embedding Visualizations

I need to ask few questions regarding word embeddings.....could be basic.

1. When we convert a one-hot vector of a word for instance king [0 0 0 1 0] into an embedded vector E = [0.2, 0.4, 0.2, 0.2].... is there any importance for each index in resultant word vector? For instance E[1] which is 0.2.... what specifically E[1] defines (although I know its basically a transformation into another space).... or word vector collectively defines context but not individually...
2. How the dimension (reduced or increased) of a word vector matters as compared to the original one-hot vector ?
3. How can we define lookup table in term of embedding layer?
4. is lookup table a kind of random generated table or it already been trained separately with respect to data instance in data and we just use it later on in Neural Network operations? 5- Is there any method to visualize an embedded vector at Hidden Layer (as we do have in Image based Neural Network Processing)?

Thanks in advance